Some studies have reported promising results on the use of Support Vector Machines (SVMs) for predicting fault-prone software components. Nevertheless, the performance of the method heavily depends on the setting of some parameters. To address this issue, we investigated the use of a Genetic Algorithm (GA) to search for a suitable configuration of SVMs to be used for inter-release fault prediction. In particular, we report on an assessment of the method on five software systems. As benchmarks we exploited SVMs with random and Grid-search configuration strategies and several other machine learning techniques. The results show that the combined use of GA and SVMs is effective for inter-release fault prediction.
A Further Analysis on the Use of Genetic Algorithm to Configure Support Vector Machines for Inter-Release Fault Prediction
SARRO, FEDERICA;FERRUCCI, Filomena;GRAVINO, Carmine
2012-01-01
Abstract
Some studies have reported promising results on the use of Support Vector Machines (SVMs) for predicting fault-prone software components. Nevertheless, the performance of the method heavily depends on the setting of some parameters. To address this issue, we investigated the use of a Genetic Algorithm (GA) to search for a suitable configuration of SVMs to be used for inter-release fault prediction. In particular, we report on an assessment of the method on five software systems. As benchmarks we exploited SVMs with random and Grid-search configuration strategies and several other machine learning techniques. The results show that the combined use of GA and SVMs is effective for inter-release fault prediction.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.